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Haplotype-aware variant calling with PEPPER-Margin-DeepVariant enables high accuracy in nanopore long-reads

Abstract

Long-read sequencing has the potential to transform variant detection by reaching currently difficult-to-map regions and routinely linking together adjacent variations to enable read-based phasing. Third-generation nanopore sequence data have demonstrated a long read length, but current interpretation methods for their novel pore-based signal have unique error profiles, making accurate analysis challenging. Here, we introduce a haplotype-aware variant calling pipeline, PEPPER-Margin-DeepVariant, that produces state-of-the-art variant calling results with nanopore data. We show that our nanopore-based method outperforms the short-read-based single-nucleotide-variant identification method at the whole-genome scale and produces high-quality single-nucleotide variants in segmental duplications and low-mappability regions where short-read-based genotyping fails. We show that our pipeline can provide highly contiguous phase blocks across the genome with nanopore reads, contiguously spanning between 85% and 92% of annotated genes across six samples. We also extend PEPPER-Margin-DeepVariant to PacBio HiFi data, providing an efficient solution with superior performance over the current WhatsHap-DeepVariant standard. Finally, we demonstrate de novo assembly polishing methods that use nanopore and PacBio HiFi reads to produce diploid assemblies with high accuracy (Q35+ nanopore-polished and Q40+ PacBio HiFi-polished).

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Fig. 1: Nanopore variant-calling results.
Fig. 2: Comparison between Nanopore, Illumina and PacBio HiFi variant calling performance.
Fig. 3: Margin and WhatsHap phasing results.
Fig. 4: Gene analysis.
Fig. 5: Diploid assembly-polishing results.

Data availability

We have made the analysis data available publicly (variant calling outputs, genome assemblies) in: https://console.cloud.google.com/storage/browser/pepper-deepvariant-public/analysis_data. The source data for the main figures can be found in: https://console.cloud.google.com/storage/browser/pepper-deepvariant-public/figure_source_data/Figure_source_data/.

For sequencing data, we used several publicly available datasets:

• GIAB consortium26,28: https://ftp-trace.ncbi.nlm.nih.gov/giab/ftp/data/

• Human Pangenome Reference Consortium (HPRC): https://s3-us-west-2.amazonaws.com/human-pangenomics/index.html

• Telomere-to-telomere consortium11,12: https://github.com/nanopore-wgs-consortium/CHM13

Please see the Supplementary Notes to find specific links to the sequencing data that we used for our analysis. Source data are provided with this paper.

Code availability

The modules of PEPPER-Margin-DeepVariant are publicly available in these repositories:

• PEPPER: https://github.com/kishwarshafin/pepper

• Margin: https://github.com/UCSC-nanopore-cgl/margin

• DeepVariant: https://github.com/google/deepvariant

The PEPPER-Margin-DeepVariant software57 is available at https://doi.org/10.5281/zenodo.5275510, and we used r0.4 version for the evaluation presented in this manuscript. For simpler use, we have also created a publicly available docker container, kishwars/pepper_deepvariant:r0.4, that can run our variant-calling and polishing pipelines.

References

  1. 1.

    Altshuler, D. M. et al. An integrated map of genetic variation from 1,092 human genomes. Nature 491, 56–65 (2012).

    Article  CAS  Google Scholar 

  2. 2.

    McKenna, A. et al. The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20, 1297–1303 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  3. 3.

    Li, W. & Freudenberg, J. Mappability and read length. Front. Genet. 5, 381 (2014).

    PubMed  PubMed Central  Google Scholar 

  4. 4.

    Chaisson, M. J. P. et al. Multi-platform discovery of haplotype-resolved structural variation in human genomes. Nat. Commun. 10, 1784 (2019).

  5. 5.

    Belton, J. M. et al. Hi-C: a comprehensive technique to capture the conformation of genomes. Methods 58, 268–276 (2012).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  6. 6.

    Falconer, E. & Lansdorp, P. M. Strand-seq: a unifying tool for studies of chromosome segregation. Semin. Cell Developmental Biol. 24, 643–652 (2013).

    CAS  Article  Google Scholar 

  7. 7.

    Weisenfeld, N. I., Kumar, V., Shah, P., Church, D. M. & Jaffe, D. B. Direct determination of diploid genome sequences. Genome Res. 27, 757–767 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  8. 8.

    Jain, M. et al. Improved data analysis for the MinION nanopore sequencer. Nat. Methods 12, 351 (2015).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  9. 9.

    Eid, J. et al. Real-time DNA sequencing from single polymerase molecules. Science 323, 133–138 (2009).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  10. 10.

    Jain, C., Rhie, A., Hansen, N., Koren, S. & Phillippy, A. M. A long read mapping method for highly repetitive reference sequences. Preprint at https://doi.org/10.1101/2020.11.01.363887 (2020).

  11. 11.

    Miga, K. H. et al. Telomere-to-telomere assembly of a complete human X chromosome. Nature 585, 79–84 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. 12.

    Logsdon, G. A. et al. The structure, function and evolution of a complete human chromosome 8. Nature 593, 7857 (2021).

  13. 13.

    Shafin, K. et al. Nanopore sequencing and the Shasta toolkit enable efficient de novo assembly of eleven human genomes. Nat. Biotechnol. 38, 1044–1053 (2020).

  14. 14.

    Cheng, H., Concepcion, G. T., Feng, X., Zhang, H. & Li, H. Haplotype-resolved de novo assembly using phased assembly graphs with hifiasm. Nat. Methods 18, 170–175 (2021).

  15. 15.

    Nurk, S. et al. HiCanu: accurate assembly of segmental duplications, satellites, and allelic variants from high-fidelity long reads. Genome Res. 30, 1291–1305 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  16. 16.

    Kolmogorov, M., Yuan, J., Lin, Y. & Pevzner, P. A. Assembly of long, error-prone reads using repeat graphs. Nat. Biotechnol. 37, 540–546 (2019).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  17. 17.

    Ruan, J. & Li, H. Fast and accurate long-read assembly with wtdbg2. Nat. Methods 17, 155–158 (2020).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  18. 18.

    nanoporetech/medaka: sequence correction provided by ONT Research, https://github.com/nanoporetech/medaka (Oxford Nanopore Technologies, 2018).

  19. 19.

    Luo, R. et al. Exploring the limit of using a deep neural network on pileup data for germline variant calling. Nat. Mach. Intell. 2, 220–227 (2020).

    Article  Google Scholar 

  20. 20.

    Edge, P. & Bansal, V. Longshot enables accurate variant calling in diploid genomes from single-molecule long read sequencing. Nat. Commun. 10, 1–10 (2019).

    CAS  Article  Google Scholar 

  21. 21.

    Wenger, A. M. et al. Accurate circular consensus long-read sequencing improves variant detection and assembly of a human genome. Nat. Biotechnol. 37, 1155–1162 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. 22.

    Ebler, J., Haukness, M., Pesout, T., Marschall, T. & Paten, B. Haplotype-aware diplotyping from noisy long reads. Genome Biol. 20, 116 (2019).

    PubMed  PubMed Central  Article  Google Scholar 

  23. 23.

    Huddleston, J. et al. Discovery and genotyping of structural variation from long-read haploid genome sequence data. Genome Res. 27, 677–685 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  24. 24.

    Sedlazeck, F. J. et al. Accurate detection of complex structural variations using single-molecule sequencing. Nat. Methods 15, 461–468 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. 25.

    Patterson, M. D. et al. WhatsHap: weighted haplotype assembly for future-generation sequencing reads. J. Comput. Biol. 22, 498–509 (2015).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  26. 26.

    Zook, J. M. et al. Extensive sequencing of seven human genomes to characterize benchmark reference materials. Sci. Data 3, 160025 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  27. 27.

    Wagner, J. et al. Benchmarking challenging small variants with linked and long reads. Preprint at https://doi.org/10.1101/2020.07.24.212712 (2020).

  28. 28.

    Olson, N. D. et al. precisionFDA Truth Challenge V2: calling variants from short-and long-reads in difficult-to-map regions. Preprint at https://doi.org/10.1101/2020.11.13.380741 (2020).

  29. 29.

    Jain, M. et al. Nanopore sequencing and assembly of a human genome with ultra-long reads. Nat. Biotechnol. 36, 338 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  30. 30.

    Jain, M. et al. Linear assembly of a human centromere on the Y chromosome. Nat. Biotechnol. 36, 321 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  31. 31.

    Fiddes, I. T. et al. Comparative Annotation Toolkit (CAT)—simultaneous clade and personal genome annotation. Genome Res. 28, 1029–1038 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  32. 32.

    Eichler, E. E., Clark, R. A. & She, X. An assessment of the sequence gaps: unfinished business in a finished human genome. Nat. Rev. Genet. 5, 345 (2004).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  33. 33.

    Euskirchen, P. et al. Same-day genomic and epigenomic diagnosis of brain tumors using real-time nanopore sequencing. Acta Neuropathol. 134, 691–703 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  34. 34.

    Rang, F. J., Kloosterman, W. P. & de Ridder, J. From squiggle to basepair: computational approaches for improving nanopore sequencing read accuracy. Genome Biol. 19, 90 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  35. 35.

    Chin, C.-S. et al. A diploid assembly-based benchmark for variants in the major histocompatibility complex. Nat. Commun. 11, 1–9 (2020).

    Article  CAS  Google Scholar 

  36. 36.

    Poplin, R. et al. A universal SNP and small-indel variant caller using deep neural networks. Nat. Biotechnol. 36, 983 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  37. 37.

    Edge, P., Bafna, V. & Bansal, V. HapCUT2: robust and accurate haplotype assembly for diverse sequencing technologies. Genome Res. 27, 801–812 (2017).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  38. 38.

    Rodriguez, O. L. et al. A novel framework for characterizing genomic haplotype diversity in the human immunoglobulin heavy chain locus. Front. Immunol. 11, 2136 (2020).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  39. 39.

    Chin, C.-S. et al. Phased diploid genome assembly with single-molecule real-time sequencing. Nat. Methods 13, 1050 (2016).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  40. 40.

    Koren, S. et al. De novo assembly of haplotype-resolved genomes with trio binning. Nat. Biotechnol. 36, 1174 (2018).

    CAS  Article  Google Scholar 

  41. 41.

    Porubsky, D. et al. Fully phased human genome assembly without parental data using single-cell strand sequencing and long reads. Nat. Biotechnol. 39, 302–308 (2021).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  42. 42.

    Harrow, J. et al. GENCODE: The reference human genome annotation for The ENCODE Project. Genome Res. 22, 1760–1774 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  43. 43.

    Zook, J. M. et al. An open resource for accurately benchmarking small variant and reference calls. Nat. Biotechnol. 37, 561 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  44. 44.

    Baid, G. et al. An extensive sequence dataset of gold-standard samples for benchmarking and development. Preprint at https://doi.org/10.1101/2020.12.11.422022 (2020).

  45. 45.

    Frankish, A. et al. GENCODE reference annotation for the human and mouse genomes. Nucleic Acids Res. 47, D766–D773 (2019).

    CAS  Article  Google Scholar 

  46. 46.

    Heller, D. & Vingron, M. SVIM-asm: Structural variant detection from haploid and diploid genome assemblies. Bioinformatics 36, 22–23 (2020).

    Google Scholar 

  47. 47.

    Zook, J. M. et al. A robust benchmark for detection of germline large deletions and insertions. Nat. Biotechnol. 38, 1347–1355 (2020).

  48. 48.

    Tewhey, R., Bansal, V., Torkamani, A., Topol, E. J. & Schork, N. J. The importance of phase information for human genomics. Nat. Rev. Genet. 12, 215–223 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  49. 49.

    Browning, S. R. & Browning, B. L. Haplotype phasing: existing methods and new developments. Nat. Rev. Genet. 12, 703–714 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  50. 50.

    Glusman, G., Cox, H. C. & Roach, J. C. Whole-genome haplotyping approaches and genomic medicine. Genome Med. 6, 1–16 (2014).

    Article  CAS  Google Scholar 

  51. 51.

    Li, H. et al. A synthetic-diploid benchmark for accurate variant-calling evaluation. Nat. Methods 15, 595–597 (2018).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  52. 52.

    Li, H. Minimap2: pairwise alignment for nucleotide sequences. Bioinformatics 34, 3094–3100 (2018).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  53. 53.

    Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  54. 54.

    Krusche, P. et al. Best practices for benchmarking germline small-variant calls in human genomes. Nat. Biotechnol. 37, 555–560 (2019).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  55. 55.

    Cleary, J. G. et al. Joint variant and de novo mutation identification on pedigrees from high-throughput sequencing data. J. Comput. Biol. 21, 405–419 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  56. 56.

    Newey, W. K. Adaptive estimation of regression models via moment restrictions. J. Econom. 38, 301–339 (1988).

    Article  Google Scholar 

  57. 57.

    K. Shafin, et al. PEPPER-Margin-DeepVariant (version r0.4), https://doi.org/10.5281/zenodo.5275510 (Zenodo, 2021).

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Acknowledgements

Research reported in this publication was supported by the National Human Genome Research Institute of the National Institutes of Health under award numbers U41HG010972, R01HG010485, U01HG010961, and OT2OD026682 (K.S., T.P., M.K., J.M.E, K.H.M., M.J., B.P.). We thank Circulomics Inc. for sharing HG001 Nanopore data. We thank J. Zook and J. Wagner from the National Institute of Standards and Technology (NIST) for providing a draft version of the HG005 v4.2.1 benchmark. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.

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Authors

Contributions

B.P. and A.C. designed and executed the study. K.S. developed PEPPER. T.P. developed Margin. P.-C.C. designed candidate import functionality in DeepVariant. K.S., T.P., and P.-P.C. contributed equally to the methods development and core analysis presented. M.N. designed alt-event alignment in DeepVariant, A.K. contributed to haplotype sorting and improvements on DeepVariant runtime, S. G. contributed to candidate import module of DeepVariant, G.B. designed and executed the post-processing model to improve multiallelic variant accuracy. M.K. designed and evaluated assembly polishing. J.M.E. designed local phasing metric and contributed to phasing evaluation. K.H.M. provided experimental design guidance, and P.C. generated assemblies and provided guidance on assembly polishing. M.J. performed nanopore sequencing, quality control and helped to design and execute analysis. All authors approve of the final manuscript.

Corresponding authors

Correspondence to Andrew Carroll or Benedict Paten.

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Competing interests

K.S. has performed paid internships at NVIDIA Corp and Google. P.C., M.N., A.K., S.G., G.B., and A.C. are employees of Google and own Alphabet stock as part of the standard compensation package. M.J. has received reimbursement for travel, accommodation, and conference fees to speak at events organized by ONT. The remaining authors declare no competing interests.

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Peer review information Nature Methods thanks Ruibang Luo and the other, anonymous, reviewers for their contribution to the peer review of this work. Peer reviewer reports are available. Lin Tang was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Shafin, K., Pesout, T., Chang, PC. et al. Haplotype-aware variant calling with PEPPER-Margin-DeepVariant enables high accuracy in nanopore long-reads. Nat Methods 18, 1322–1332 (2021). https://doi.org/10.1038/s41592-021-01299-w

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